Long Range Wide Area Network (LoRaWAN) is a promising communication technology for environmental monitoring due to its low power consumption and long-range capabilities. Despite its advantages, several challenges are associated with LoRaWAN due to technical limitations, environmental factors, and operational complexities. Continued advancements in adaptive algorithms and AI-based optimization are essential for overcoming these challenges and fully realizing the potential of LoRaWAN in diverse IoT applications. Transmission parameter allocation is one of the most studied aspects of LoRaWAN, typically required to reduce energy consumption and improve the signal quality in dense LoRaWAN. Evaluations of the optimization algorithms for parameter allocation are usually done using simulators. However, they do not imitate the dynamic nature of the network environment and other signal characteristics. Thus, it becomes difficult to understand the performance of these algorithms when deployed on real devices. This paper introduces transmission parameter allocation strategies using a State–Action–Reward–State–Action (SARSA) and Deep Q-Learning Network (DQN) based Reinforcement Learning (RL)-based scheduling algorithm for allocating transmission parameters in LoRaWAN communication. We experimentally evaluate this algorithm in a water quality monitoring system using actual LoRaWAN devices to assess Signal to Noise ratio (SNR), Received Signal Strength Indicator (RSSI), Time on Air (ToA), and power consumption of our RL-based algorithms with the default and Adaptive Data Rate (ADR) in LoRaWAN communication.

Beyond 5G and future 6G aim to address rising energy demands as the world becomes more interconnected. LoRaWAN network is an energy-efficient IoT solution, but frequent retransmissions can quickly deplete sensor batteries. Efficient traffic management and collision avoidance are crucial. Initially, LoRaWAN used ALOHA for multi-access, causing increased network collisions. The recent innovation of Channel Activity Detection (CAD) has emerged to tackle these issues. CAD enhances multi-access by sensing channel activity before transmission. Although an improvement, CAD is not foolproof. Our paper introduces enhancements to CAD through the Deep Deterministic Policy Gradient-based Algorithm (DDPG_CAD). To assess CAD functionality, we develop LoRaCAD, a dedicated simulator. We also conduct a thorough comparative analysis of scheduling strategies, considering energy efficiency, latency, and packet delivery ratio.

The Internet of Things (IoT) is a burgeoning field for study and experimentation. It allows users to create and receive a wide bevy of information from a massive array of devices. But as the IoT network gets denser, a load-balancing algorithm is required to keep itself running smoothly. Load Balancing is all but required in large IoT networks to avoid a part of servers getting overloaded and others being free. Existing solutions show both heuristic and machine learning algorithms designed for load balancing. Static algorithms go well with traditional IoT networks but are not built to scale dynamically and respond to loads. Dynamic heterogeneity and massive IoT disrupt load balancing. Machine learning-based algorithms have proven to give better scheduling solutions and improve performance in such networks. To analyze the performance of machine learning algorithms over heuristic ones, we designed an experimental testbed using a POX SDN controller and Mininet. We also show results confirming that the machine learning-based algorithms are better in terms of packet loss and response time.

In this paper we show a low cost and environmentally friendly fabrication for an agricultural sensing application. An antenna, a soil moisture sensor, and a leaf wetness sensor are inkjet-printed on paper substrate. A microprocessor attached to the paper substrate is capable of detecting the capacitance change on the surface of the sensor, and report the data over the wireless communication interface. This sensing system is useful to optimize irrigation systems.